Downscaling of land use change scenarios to assess the dynamics of European landscapes

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churchuiversine 10Europes rural areas are expected to witness massive and rapid changes in land use due to changes in demography, global trade, technologyAgriculture, Ecosystems and Environmand enlargement of the European Union. Changes in demand for agricultural products and agrarian structure are likely to have a large impacton landscape quality and the value of natural areas. A spatially explicit, dynamic, land use change model has been used to translate Europeanlevel scenarios into a high resolution assessment of changes in land use for the 25 countries of the European Union. Scenarios differ inworldview, ranging from enhanced global cooperation towards strong regionalisation on one hand and strong to weak governmentintervention on the other. Global economic and integrated assessment models were used to calculate changes in demand for agriculturalarea at country level while a spatially explicit land use change model was used to downscale these demands to land use patterns at 1 km2resolution. The land use model explicitly accounts for the variation in driving factors among countries and the path dependence in land usechange trajectories. Results indicate the large impact abandonment of agricultural land and urbanization has on European landscapes and thedifferent scenarios indicate that spatial policies can make an important contribution to preserve landscapes containing high natural and/orhistoric values. Furthermore, the dynamic simulations indicate that the trajectory of land use change has an important impact on resultinglandscape patterns as a result of the path-dependence in land use change processes. The results are intended to support discussions on thefuture of the rural area and identify hotspots of landscape change that need specific consideration.# 2005 Elsevier B.V. All rights reserved.Keywords: Europe; Downscaling; Land use change; Scenarios; Landscape1. IntroductionEurope has a varied and dynamic landscape in whichagriculture is one of the dominant land use types. Theenvironmental and social variability within Europe, incombination with a variety of agricultural policies, hascreated a complex and often dynamic pattern of land use.Since land use is the result of human decisions, the patternsof land use reflect the decision-making processes by thosewho control land resources. Agricultural policies such as theavailability of subsidies, fixing of quotas on food production,the setting aside of land in return for monetary compensationand schemes to encourage farms to diversify, have causedrapid changes in European landscapes over the past 50 years.More recently, European integration and globalizationprocesses are accelerating, e.g. in 2004, 10 new memberstates (the accession countries) entered the European Unionbringing about a larger internal market and the challenge tobridge socio-economic differences between older and newermember states. These processes will have an impact on theEuropean landscapes: spatial development and planningpolicies have to keep pace with and attempt to provide somecontrol over these developments.Many studies have confirmed that massive changes in theEuropean countryside are to be expected. The well knownstudy Ground for Choices in the early 1990s showed anenormous decrease in agricultural area for the member statesof the European Union for all scenarios considered uponreform of the Common Agricultural Policy and supposedoptimization of production practices (Rabbinge et al., 1994;Latesteijn, 1998). Although this study may have over-* Corresponding author. Tel.: +31 317485208; fax: +31 317482419.E-mail address: Peter.verburg@wur.nl (P.H. Verburg).0167-8809/$ see front matter # 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.agee.2005.11.024Downscaling of land usethe dynamics of EPeter H. Verburg *, C.J.E. SDepartment of Environmental Sciences, Wageningen UnAvailable onliAbstractange scenarios to assessopean landscapeslp, N. Witte, A. Veldkampty, P.O. Box 37, 6700 AA Wageningen, The NetherlandsJanuary 2006www.elsevier.com/locate/ageeent 114 (2006) 3956Such assessments cannot merely be based on local casestemestimated the adaptation of agricultural technology, recentstudies by Rounsevell (Rounsevell et al., 2005) and vanMeijl et al. (this issue) still indicate considerable decreasesin agricultural area for most of the studied scenarios. Theseprojections have raised enormous concerns about rurallivelihoods and the contribution of the current agriculturalareas in terms of nature conservation, biodiversity and theEuropean landscape (Vos and Meekes, 1999). Furthermore,European landscapes are threatened by unprecedented ratesof urbanization and sub-urbanization (Antrop, 2004;Wasilewski and Krukowski, 2004) while at the same timepolicies are anticipated to better protect natural areas andvaluable landscapes (Jongman, 1995). Traditional land-scapes are changing with increasing speed and an importantcultural heritage is becoming lost. New landscapes replacethe traditional ones gradually or sometimes abruptly(Eetvelde and Antrop, 2004).A multitude of studies have addressed the concerns ofchanges in land use and landscape in Europe. However, onlyfew have covered the whole extent of Europe (Rabbinge andVan Latesteijn, 1992; Rounsevell et al., 2005) while manystudies investigate specific processes of land use change inlocal case studies (Burgi, 1999; Peppler-Lisbach, 2003;Hietel et al., 2004; Kristensen et al., 2004). Such local casestudies of landscape change have a tendency to focusprimarily on cases that are exemplary for the studiedprocesses or where land use change leads to severeenvironmental problems (Burgi et al., 2004). Furthermore,most local studies focus on historical changes in landscape(Burgi, 1999; Tress et al., 2001), are not necessarilyrepresentative for large areas and cannot provide informa-tion on the aggregate impact of these changes at theEuropean level. On the other hand, the existing studies at theEuropean scale provide an overview of the main land usechanges but fail to integrate the different processes ofchange and are conducted at such coarse spatial andtemporal scales that they cannot provide insight into theconsequences of the foreseen changes on the landscapes.The observed gap between European level explorationsof future changes in agricultural area and local case studiesevaluating landscape impacts of ongoing processes, mostlybased on historic observations, is apparent and calls fordownscaling approaches that link the European leveldevelopments to landscape level impacts. Such downscalingis essential to adequately capture the enormous variability inlandscapes across Europe. A gross estimate of, for example,5% decrease in agricultural area within the next 20 years isnot likely to affect all regions in a similar way. Even, givensuch European level changes, it is not unlikely that certainregions experience an increase in agricultural area. Down-scaling will allow an assessment of these differentialdevelopments and enable the identification of criticalregions and places that are most vulnerable to the effectsof these changes. The impacts on landscapes and other socialand environmental indicators can often not be based on theP.H. Verburg et al. / Agriculture, Ecosy40coarse scale assessments since most impacts are locationstudies since case study location selection is often biased tothe presence of the phenomenon that is studied and resultscannot easily be extrapolated to the European extent.Therefore, the downscaling of European level assessmentsof land use change is essential to understand variationsbetween locations and make assessments of European levelimpacts. Finally, downscaling provides both physical andstrategic planners with the tools that are required to envisagethe outcome of particular trends and assess the implicationsof alternative decisions and planning strategies at differentspatial scales (Stillwell and Scholten, 2001).To our knowledge there are no studies published inliterature that downscale European level scenarios of futurechange in political and socio-economic conditions to aresolution suitable for detecting landscape change. Oneexception is the recently published scenario study for theoriginal 15 countries of the European Union by Rounsevellet al. (2005). However, this downscaling effort, based onsimple land allocation rules, does not downscale beyond aspatial resolution of 10 min (approximately 16 km). Thisresolution does not allow the identification of land usechange effects at the landscape level and is insufficient toestablish a link with local case studies.This paper presents a study that employs a high resolutionland use change model to downscale land use changes frommacro-scale models to the landscape level.2. Methods and data2.1. Overview of the approachIn contrast to the Ground for Choices study published inthe early 1990s (Rabbinge and Van Latesteijn, 1992;Rabbinge et al., 1994) the current study does not aim atpresenting optimized options for European land use given aset of goals and policy objectives. Moreover, this studyintends to provide a procedure to visualize and exploredifferent, plausible, developments in land use in Europe.Therefore, a scenario-based approach was chosen. Scenariosfollow the concept storylines of the IPCC Special Report onspecific and dependent on the spatial patterns of land use.Impacts on biodiversity of natural areas not only depend onthe overall change in nature area but also on the change inspatial configuration of the natural areas, determining therelative connectivity or isolation of the natural areas(Wimberly and Ohmann, 2004). Changes in livelihood inareas that face abandonment of agricultural land areexpected to be highly variable, due to the spatialconcentration of land abandonment. Impacts of suchdevelopments on carbon sequestration and land degradationare highly dependent on the actual soil and landscapeconditions at the locations of abandonment; thereforerequiring high resolution assessments of land use change.s and Environment 114 (2006) 3956Emission Scenarios (SRES) (IPCC, 2000) which arestructured along two axis yielding four scenarios distin-guishing globalization from regionalisation; and develop-ment pursuing narrowly defined economic objectives frommore broadly defined economic, social and environmentalobjectives. However, the focus of these scenarios iscompletely outside land use, agriculture and rural develop-ment and lacks the regional disaggregating needed for thisstudy. Therefore the scenarios were elaborated for land useissues and agricultural policies typical for Europe (Westhoeket al., this issue). This resulted in a series of four scenariosdistinguished by different degrees of global (market)integration and different levels of (policy) regulation. Theregulation level is indicative for the ambition of govern-ments in pursuing its goals with ambitious regulation, e.g. toobtain equity or environmental sustainability. Scenarios witha relatively low level of regulation include the A1 (GlobalEconomy) and A2 (Continental Market) scenarios. Theother two scenarios: B1 (Global Co-operation) and B2(Regional Communities) assume a relatively high level ofregulation, including specific spatial and agriculturalpolicies.The storylines of the scenarios were scaled down toassess the effects on land use patterns by a series ofas a consequence of different contextual conditions, specificvariation in the socio-economic and biophysical conditions,and the influence of land use history and culture (Nassauer,1995; Naveh, 2001). Since no single method can address thedifferent processes at these different scales consistently asequence of models was used at different scales (Fig. 1). Thedemand for agricultural production, development ofproduction levels in agriculture, demand for urban andindustrial area and changes in acreage of natural areas werecalculated on a national scale taking into account Europeanlevel and global level conditions and interactions. For thispurpose use was made of a combination of a macro-economic model and an integrated assessment model (vanMeijl et al., this issue). The actual downscaling of thenational level changes to the landscape level was done by aspatially explicit land use change model. For each country(or country-group) the assessment was done separately to beable to account for the specific driving factors of land usechange in each country.2.2. Land use change modelThe land use and land cover change research communityP.H. Verburg et al. / Agriculture, Ecosystems and Environment 114 (2006) 3956 41ve dessimulation models that account for the hierarchical structureof land use driving factors. Global trade agreements andpolitical structures may be an important factor explainingdifferences in agricultural and industrial developmentamong continents and countries while local variations insocial and biophysical conditions are important determi-nants of landscape patterns and variability. Furthermore, thedriving factors of landscape pattern are often region-specificFig. 1. Representation of the hierarchical procedure to translate the qualitatipatterns.has developed a wide range of spatially explicit land usechange models over the past decade. Reviews are providedby Briassoulis (2000) and Verburg et al. (2004b). Thesemodels differ in the spatial resolution and extent, underlyingconcept and the range of applications. For the studydescribed in this paper the CLUE-s model (Conversion ofLand Use and its Effects model version CLUE-s 2.3) hasbeen chosen because of its flexibility in configuration, andcription of scenarios into the quantitative simulation of impacts on land usestemP.H. Verburg et al. / Agriculture, Ecosy42the ability to specify the scenario conditions in detail(Verburg et al., 2002; Verburg and Veldkamp, 2004a).Furthermore, the model has been widely used by differentresearch groups and has been validated in different cases(Kok et al., 2001; Verburg et al., 2002). The CLUE-s modelis based on the dynamic simulation of competition betweenland uses while the spatial allocation rules can be specifiedbased on either an empirical analysis, user-specifieddecision rules, neighborhood characteristics (similar tocellular automata models (Verburg et al., 2004c)) or acombination of these methods. The actual allocation is basedon the constraints and preferences defined by the user basedon the characteristics of the land use type or the assumedprocesses and constraints relevant to the scenario. In thefollowing sections we describe the specification of modelFig. 2. Schematic representats and Environment 114 (2006) 3956parameterization in more detail. Differences betweenscenarios are obtained by differences in data inputs andvariable settings that affect the behavior of the model.Therefore, the parameterization is central to the downscalingprocedure. Four categories of settings and data inputs can bedistinguished that together define the set of preferences andconstraints for which the allocation routine determines anoptimal solution (Fig. 2).2.2.1. Land requirementsThe land requirements of the different land use typesdetermine the actual area of the different land use types thatis allocated by the model. These demands are specified foreach country or country-group. Fig. 3 shows the countriesincluded in this study. In order to limit the number ofion of model structure.ystemshis stucountries distinguished in the macro-economic model (vanP.H. Verburg et al. / Agriculture, EcosFig. 3. Overview of the countries included in tMeijl et al., this issue), the Baltic countries were dealt withas one group as well as Belgium and Luxembourg. Changesin agricultural land areas are based on the results of thecombined simulations with a macro-economic (GTAP) andintegrated assessment model (IMAGE) as described by vanMeijl et al. (this issue). GTAP calculates the economicconsequences for the agricultural system by capturing staticfeatures of the global food market, with the dynamics fromexogenous scenario assumptions. The output from GTAP isused by the IMAGE model to calculate yields, the demandfor land, feed efficiency rates and environmental indicators.The output of GTAP/IMAGE cannot be directly used asland requirements for the downscaling procedure. Ingeneral, the current agricultural area has a larger extent inthe spatial data used in the downscaling procedure ascompared to the area reported in the statistical sources thatare used in the macro-economic calculations. This isbecause the spatial data incorporate landscape elements thatare smaller than the mapping resolution within the mappingunits, e.g. ditches, roads and farm houses. Also temporarilyfallow land and non-cultivated field borders as part of theset-aside policy are not distinguished in the spatial data.Therefore, a correction is made to the output of the GTAP/IMAGE models to account for these differences betweenstatistical and spatial data based on the current situation.Growth in built-up area is calculated proportional tochanges in population, GDP and the growth in the industrial/services sectors calculated by the macro-economic model.Changes in natural area follow land availability afterand Environment 114 (2006) 3956 43dy and indication of the accession countries.accounting for changes in agricultural and built-up area. Ifland is available, nature development can occur spontaneouson abandoned lands or more directly through activemanagement of former agricultural areas. See Table 1 fora specification of these conditions.2.2.2. Location suitabilityWhereas the demand for land by the different land usetypes determines the overall competitive capacity of thedifferent land use types, the location suitability is a majordeterminant of the competitive capacity of the different landuse types at a specific location. It is well known that a widerange of local and more regional factors can influence thesuitability of a location for a land use type (Lambin et al.,2001; Burgi et al., 2004). Besides the commonly consideredbiophysical suitability in terms of crop growth potential,other factors, such as accessibility or neighborhoodcharacteristics, should be considered as factors influencingthe suitability as perceived by the decision maker. In thisstudy the suitability is determined by a scenario and land usetype specific combination of empirical analysis, neighbor-hood conditions and decision rules. The final suitability is aweighted average of the suitability based on empiricalanalysis capturing the historic and current locationpreferences in response to location characteristics, theinfluence of neighboring land uses on location suitability(e.g. in case of agglomeration effects) and scenario specificsuitabilities based on scenario specific decision rules. It isP.H.Verburgetal./Agriculture,EcosystemsandEnvironment114(2006)395644Table 1Overview of the scenario conditions relevant to the spatial model that have been used in the simulationsA1 (Global Economy) A2 (Continental Market) B1 (Global Co-operation) B2 (Regional communities)Land requirementsArable area and grassland Calculated by GTAP/LEITAP/IMAGE simulations, correctedfor abolished set-aside policiesin 2010 for arable areaCalculated by GTAP/LEITAP/IMAGE simulations, correctedfor abolished set-aside policiesin 2020 for arable areaCalculated by GTAP/LEITAP/IMAGEsimulations, corrected for abolishedset-aside policies in 2020 for arable areaCalculated by GTAP/LEITAP/IMAGEsimulations, set-aside policyfor arable area is continuedBuilt-up area Function of population growth,GDP growth and growth inthe industrial and servicessectors as calculated byGTAP/LEITAPFunction of population growth,GDP growth and growth in theindustrial and services sectorsas calculated by GTAP/LEITAPFunction of population growth, GDPgrowth and growth in the industrialand services sectors as calculated byGTAP/LEITAPFunction of population growth,GDP growth and growth in theindustrial and services sectorsas calculated by GTAP/LEITAPNature (forest/nature/natural grasslands)50% of land that has beenabandoned 10 years earlieris (spontaneously) convertedto nature50% of land that has beenabandoned 10 years earlieris (spontaneously) convertedto nature50% of newly abandoned land is (actively)converted to nature. 50% of land that hasbeen abandoned 10 years earlier is(spontaneously) converted to nature50% of newly abandoned landis (actively) converted tonature. 50% of land that hasbeen abandoned 10 yearsearlier is (spontaneously)converted to natureInland wetlands and others(incl. beaches, rocks,snow/glaciers)Extent and location areconstantExtent and location areconstantExtent and location are constant Extent and location are constantArea specific policiesNature reserve protection Main nature reserves areprotectedMain nature reserves areprotectedNature within Natura2000 areasis protected. Conversion of pastureto arable land or built-up land isnot allowed in Natura2000 areasMain nature reserves and bufferzones are protectedErosion reduction policies No specificarrangementsNo specificarrangementsIncentives to convert arable landto grassland or abandonment inerosion sensitive areas. No newarable land conversion in highlyerosion sensitive areasIncentives to convert arable landto grassland or abandonment inerosion sensitive areas. No newarable land conversion in highlyerosion sensitive areasNature development Some incentives to avoidfragmentation of naturalareasSome incentives to avoidfragmentation ofnatural areasIncentives for conversionof arable land into naturewithin Natura2000 areas, newbuilt-up area not allowed withinNatura2000 areasIncentives to convert abandonedfields within less favoured areasto nature (landscapeelements/patches). Incentiveto develop small patches ofnature/landscape elementsin the agricultural landscapeP.H. Verburg et al. / Agriculture, EcosystemsSpatialpoliciesinfluencingagriculturalareasLessfavouredareassupportabolished;structuralchangesinagricultureprovideincentivestoallocatearablelandsandgrasslandsinareaswithrelativelyhighpotentialproductivityLessfavouredareasaremaintainedleadingtoincentivesforcontinuationofarableagricultureandmanagedgrasslandsintheseareasInlessfavouredareasonlytheincentivesformanagedgrasslandsremainthroughincorporationofthemaingrasslandareasinLFAsintheNatura2000networkLessfavouredareasaremaintainedleadingtoincentivesforcontinuationofarableagricultureandmanagedgrasslandsintheseareasUrbanizationpoliciesDominanturbanizationpatternSprawledurbanizationpattern:urbancentersgrowstrongly,proliferationofnewbuilt-upareainneighborhoodofforest/pastureareas(secondhouses)Sprawledurbanizationpatterns:urbancentersgrowstrongly,proliferationofnewbuilt-upareainneighborhoodofforest/pastureareas(secondhouses)Compacturbanizationpatterns:relativestronggrowthofprovincialtownsCompacturbanizationpatterns,relativestronggrowthofprovincialtowns assumed that in the different scenarios the decision makersmay have a different perception of suitability as result ofchanges in worldview, policy incentives and extension. Asan example, in the A1 (Global Economy) scenario it isassumed that potential crop productivity may be a moreimportant factor determining suitability than in otherscenarios. The empirical analysis is used to capture thecurrent and historic preferences for locations based on alogistic regression relating land use patterns to a wide rangeof potential factors that are expected to determine thelocation suitability (Verburg et al., 2004e). Logisticregression is a very common technique in land use changestudies to quantify the relation between driving factors andland use (change) patterns (Nelson et al., 2001; Serneels andLambin, 2001; Munroe et al., 2002). The full list of locationcharacteristics included in this analysis can be found inTable 2. The analysis was made for each land use type andeach country (group) separately to allow different factors tobe a determinant of land use patterns in different countries.This approach was chosen because previous studies on thedriving factors of land use have revealed that many of thesefactors are dependent on the context and different regionsoften show very different relations (de Koning et al., 1998;Verburg and Chen, 2000; Lambin and Geist, 2003; Geist andLambin, 2004). This was confirmed by the results of theanalysis: different factors were significantly related to theland use distribution in different countries. In general theregression models explained the current land use distributionwith a reasonable to very good fit as measured by receiveroperating characteristic (ROC) values ranging between 0.6and 0.95. The thus derived empirical relations do capture thecurrent structure of land use and the response of this tochanges in dynamic location factors (such as population forwhich projections are made), but does not allow for changesin spatial behavior as is assumed in the different scenarios orthe impacts of region specific policies. The latter isaccounted for by the specification of area-specific conditionsas described below while changes in behavior are dealt withby adapting the calculated suitability with decision rules thatreflect the assumed changes in location preferences. Theseinclude the assumed preference of agriculture for regionswith high potential productivity in scenario A1 (GlobalEconomy) and the different preferences for new built-upareas and urbanization policies described by neighborhoodfunctions (Table 1).2.2.3. Land use type specific conditionsLand use types often have specific characteristics thatinfluence their conversion and that cause differences in theirspatio-temporal behavior. While urban growth in almost allcases results in a one-way conversion of other land uses intobuilt-up area, arable area can still increase in part of theregion while the region as a whole faces a decrease.Therefore each land use type is characterized in the modelby a conversion elasticity and a set of plausible conversions.and Environment 114 (2006) 3956 45Conversion elasticities ensure that the current land useP.H. Verburg et al. / Agriculture, Ecosystems and Environment 114 (2006) 395646Table 2List of all variables used in the analysisName Description SourceAccessibility 1 Traveltime to cities with morethan 100.000 inhabitantsAccessibility analysis based on GISCOdatabase infrastructureAccessibility 2 Traveltime to cities with morethan 500.000 inhabitantsAccessibility 3 Traveltime to ports with morethan 15.000 kt/year of freightAccessibility 4 Traveltime to cities with morethan 650.000 inhabitantsAccessibility 5 Airline distance to nearest main road(mainly highways)Accessibility 6 Traveltime to major airportsAccessibility 7 Traveltime to major airports andmajor portsSoil_aglim 1 Dominant limitation to agricultural use Soil Geographical Database of theEuropean Soil Bureau (ESB)(CEC, 1985; King et al., 1994)Soil_aglim 2 Secondary limitation to agricultural useSoil-impermeable Presence of an impermeablelayer within the soil profileParent material First level dominant parent material codeTexture Dominant surface textural classWater regime Dominant annual soil water regime classSoil_slope Dominant slope classTemperature Average temperature (in 8C) ELPEN database(http://www.macaulay.ac.uk/elpen)Total rain Yearly precipitation (in mm)Summer rain Total rain during the summerseason (3 months) (in mm)Growing rain Total rain during the growingseason (6 months) (in mm)Cold months Count of months with averagetemperature 15 8CBiogeograhic Biogeographical regions European Topic Centre on NatureProtection and BiodiversityDEM Height (in m) USGS GTOPO30(http://edcdaac.usgs.gov/gtopo30)Slope Slope (based on DEM, in degrees)Environ_region Environmental regions Metzger et al. (2005)Traveltime 120 Number of people that reach alocation within 120 min by drivingELPEN database(http://www.macaulay.ac.uk/elpen)Traveltime 30 Number of people that reach alocation within 30 min by drivingTraveltime 60 Number of people that reach alocation within 60 min by drivingPopulation Population density distribution Dobson et al. (2000)Population-potential Gaussian population potential Calculated based on Dobson (2000)LFA Less favoured areas (LFA) EU15: GISCO database; accession countries:delineation based on similar criteriaPrecipitation Precipitation, mean 19611990 (in mm) New et al. (1999)Temperature Temperature, mean 19611990 (in 8C)Protected areas Protected areas for the low protection scenarios Based on WCPA database of protectedareas (http://www.unep-wcmc.org/wdbpa)Erosion_risk Erosion risk for arable land Calculated following procedures of SERAE(Joint Research Centre)Natura2000 An approximation of the Natura 2000 map Created based on WCPA database and CDDANationally Designated Areas databaseystemspattern is an important determinant of future land uses as hasfrequently been indicated in land use change literature(McConnell et al., 2004). Unrealistic conversions are notallowed while others are only allowed in designated areas.Some other conversions are only realistic after a minimumtime period: the spontaneous development of nature onabandoned farmland does not directly lead to a land covertype that can be classified as nature. Therefore, it is specifiedthat agricultural lands should be abandoned for at least 10years before a (spontaneous) change into nature is possible.2.2.4. Area specific conditionsMany spatial policies aim at specific areas. The mainarea-specific policies included in this study have been listedin Table 1. Some of these policies are implemented in themodel as a restriction on all conversions in the specifiedareas (e.g. nature reserves) or as a restriction on specificconversions (e.g. no new arable land in erosion sensitiveareas). Other spatial policies are implemented as an increasein the suitability for one or more of the land use types in thespecific area: compensation of farmers in less favoured areasis implemented as an increase in location suitability relativeto locations outside the less favoured areas.2.3. Dynamic simulation of scenariosAfter the specification of the inputs and parameters of thesimulation model in accordance with the scenario description,the allocation algorithm allocates the required land areas ofthe different land use types synchronously accounting for thedefined constraints and suitability maps in an iterativeprocedure (Verburg and Veldkamp, 2004a). All simulationsare made for 2-year steps and after each time step the locationfactors and land use patterns are updated. Steps of 2 yearswere chosen instead of the traditional yearly time-steps toreduce simulation time while retaining sufficient temporalresolution. The dependence of land allocation on land use inthe previous time step through the conversion elasticity,neighborhood interactions and irreversible conversions leadsto a high level of path-dependence in the simulated land usechange trajectories. Research of complex systems involvinghuman-environment interactions and land use systems ingeneral has indicated that specific attention for the temporaldynamics and the trajectories of change is essential toproperly describe the functioning of the system (Turner et al.,2003; Rindfuss et al., 2004; Verburg et al., 2004d). The focusof this study on the emergence of changes in land use patternsrequires the use of such dynamic simulation methods andmuch simpler land use simulation algorithms commonly usedin continental or global studies would be inappropriate(Veldkamp et al., 1996).2.4. DataThe initial land use map is based on the Pan-EuropeanP.H. Verburg et al. / Agriculture, Ecosdatabase at a resolution of 250 m (Mucher et al., 2004). Foralmost the whole territory of the 25 European Unioncountries this database originates from the CORINE landcover database (CEC, 1994) while the areas not covered bythe CORINE database were based on the PELCOM database(Mucher et al., 2000). The categories in this database weremerged in such a way that a good fit with the sectorsdistinguished in the macro-economic and integratedassessment models was achieved. The macro-economicmodel is based on a classification in economic sectors andagricultural commodities that differ from the representationof land cover types in the spatial database (van Meijl et al.,this issue). This restricted the number of land cover types toeight including: built-up area; non-irrigated arable land;irrigated arable land; pasture; a class containing all forests,natural grasslands and other natural areas; inland wetlands;abandoned farm land; and a class with other land use typesthat were assumed to remain stable during the scenarioperiod, including beaches, rocky areas, bare land andglaciers. The CORINE land use map classifies large areas ofEurope as heterogeneous agricultural areas, includingcomplex cultivation patterns and agricultural areas withsignificant areas of natural vegetation. The future demandfor such land use classes could not be derived from thesector-oriented demand calculations. Reclassification ofthese categories into the agricultural category would lead toan overestimation of the agricultural area and a faultyrepresentation of the typical characteristics of these land-scapes. These complex landscapes are classified as suchsince they represent landscapes with high spatial variabilitydue to small scale landscape units (e.g. patches of nature in amatrix of mixed agriculture) or strong connections betweenlandscape elements (e.g. landscapes in which fields arebounded by hedgerows). As a consequence of the procedureused in the creation of the CORINE land cover map and thedifferent landscapes included in these categories, thecomposition of these heterogeneous agricultural areas variesthroughout Europe. The areas were reclassified based on theassumed prevalence of the different land use types withinthese classes which was based on a comparison of the arableand pasture areas in the CORINE database with nationallevel statistics. The prevalence of the heterogeneousagricultural areas were determined for each country andthe individual land cover types were randomly allocatedwithin the considered mapping units. This reclassificationresulted in a representation of the heterogeneous agriculturalareas by a patchy landscape with a country and class specificprevalence of the individual land use types.After reclassification the map was aggregated from250 m to 1 km resolution. Aggregation procedures based ondominance can cause bias in the data representation sinceless-frequently occurring land use types tend to diminish infavour of the more dominant land use types (Moody andWoodcock, 1994). This effect was minimized using aconstrained aggregation procedure in which the prevalenceof the different land cover types was determined by theand Environment 114 (2006) 3956 47national level prevalence based on the original map.stemThe land use simulations result in 2-yearly maps of landuse at 1 km2 resolution for the whole European Union(EU25). These maps provide important information on thechanging spatial patterns of land use and are the basis for theassessment of the potential impacts of these changes. Multi-temporal analysis of the maps reveals the trajectories of landuse change and leads to the identification of regions that canbe considered as potential hotspots of land use change.Hotspots were identified for all major conversions inEuropean land use: urbanization, agricultural abandonmentand changes in the natural areas. Based on this analysis themost striking results for each scenario are summarized.3.1.1. Global Economy (A1)Most striking in the A1 scenario is the large extent ofurbanization. The urbanization is a result of high populationgrowth, high economic growth leading to a larger use ofspace per person (e.g. due to the demand for shopping andrecreation facilities) and growth in the industry and servicessector. Urbanization is found throughout the whole ofEurope with hotspots located near to the main cities andagglomerations such as the Dutch Randstad and the FlemishDiamond. The absence of spatial policies to control urbansprawl causes urbanization to have large influences on thelandscapes in many parts of Europe. Since abandonment ofThe location factors that were assumed to be determi-nants of the land use patterns are derived from a wide rangeof different data sets. Only few consistent datasets areavailable for the whole extent of 25 countries. Since theselection of location factors and the simulations wereconducted for each country (group) separately some datasetswere used that did not cover the whole extent of the studyarea. This prevented that important factors that were notavailable for the complete extent had to be disregardedaltogether. Some other datasets were, however, required tofully cover the area to obtain consistent simulations acrossthe countries. An example is the delineation of the lessfavoured areas policy. Within the designated less favouredareas farmers are eligible for compensation for the lessfavourable farming conditions. At the time of study the lessfavoured areas in the accession countries were not yetformally defined. Therefore, an approximation was made byapplying the official criteria used to delineate the lessfavoured areas to the topographic and demographicconditions in the accession countries. A similar approxima-tion was made for delineating the areas that are part of theNatura2000 network of protected areas. Table 2 provides alist of all variables included in the analysis and a shortdescription of the origin of the data.3. Results3.1. Scenario specific resultsP.H. Verburg et al. / Agriculture, Ecosy48agricultural land is found in most countries the futurefunction of these lands is an important discussion item. Theabandoned lands are partially used for residential, industrialand recreational purposes, while in less accessible areas withlow population pressure spontaneous development of natureis expected. This leads to an expansion of some of the largernatural areas of Europe. Agriculture is expected to disappearfrom many of the least productive areas in Europe.3.1.2. Continental Market (A2)The A2 scenario is characterized by high pressure onavailable land resources. In spite of a slight decrease inpopulation numbers, requirements for build-up area increasedue to strong economic growth and increases in prosperityleading to a sprawled spatial pattern of urbanization (e.g.proliferation of second houses). At the same time the highprotection level for European agriculture as well as theglobal macro-economic conditions cause an increase in landrequired for agricultural purposes. In many countries thecombined requirements for agricultural and residential/commercial purposes cause that the conversions come at thecost of natural areas. Mostly the small patches of nature andlandscape elements (most likely including small patches ofnature and hedgerows) that remain within the primeagricultural areas will be lost first. Therefore, it is expectedthat the conditions of this scenario have an importantnegative impact on the natural and cultural-historical valuesof the European landscapes.3.1.3. Global Co-operation (B1)In the B1 scenario urbanization has fewer impacts on therural landscapes. This is due to the lower requirements forresidential/commercial areas compared to the A scenarios.At the same time the spatial policies that are assumed underthis scenario (see Table 1) aim at concentrating urbanizationin designated areas, leading to compact urbanizationpatterns. Other policies in this scenario aim at reinforcingthe natural values and ecological strengths of natural areasdesignated in the Natura2000 network. Natura2000 is aEuropean network of protected sites, which represent areasof the highest value for natural habitats and species of plantsand animals, which are rare, endangered or vulnerable in theEuropean Community. Large areas of abandonment ofagricultural lands offer opportunities to actually implementthese policies. Land abandonment is the result of the macro-economic conditions in combination with increasingproductivity leading to strong decreases in land requiredfor agricultural purposes. The results suggest a significantreinforcement of the designated protected natural areas atthe cost of agricultural area that is concentrated in the primeagricultural regions.3.1.4. Regional Communities (B2)This scenario shows relative modest changes in landscapepatterns due to the low rate of urbanization, policies tomaintain agricultural production in the less favoured areass and Environment 114 (2006) 3956and no policies to establish an European level network ofP.H. Verburg et al. / Agriculture, EcosystemsTable 3Total area changed due to land use change across the European Union for thedifferent scenariosScenario % of land area changed between 2000 and 2030All EUcountriesOld EUcountries (EU15)AccessioncountriesA1 7.65 7.15 9.83A2 4.74 4.53 5.62B1 8.07 8.51 6.19B2 6.02 6.30 4.79natural areas. Land abandonment is, therefore, founddistributed over different landscapes. Modest increases inagricultural productivity in combination with the decrease ofagricultural area offers opportunity to maintain diversity,natural and culturalhistorical values in most rural areas.3.2. Comparison of the scenariosThe interplay between demand for agricultural and urbanland, spatial policies and competition among land uses leadsto differences in land use dynamics between the scenarios.Table 3 indicates which part of the land area of the EU isexpected to face some kind of change in land use between2000 and 2030. This table indicates a tremendous impact onland use in this period: even in the scenario with the smallestdynamics (A2) almost 5% of the total land area will beconverted to another land use type. Note that this onlyincludes conversions between the legend classes used in thisstudy; other conversions that do not change our classificationof the land use, e.g. between crop types or residential andindustrial functions, are not counted. Due to the large area ofland abandonment the B1 scenario is most dynamic resultingin large changes in land use patterns. These changes canhave a huge impact on the aesthetic and functional quality ofthe landscapes. Another pattern of interest is the relativestrength of land use dynamics in the 15 countries that weremember of the European Union before 2004 (EU15) versusthe accession countries. Whereas the accession countriesshow more dynamics in the A scenarios compared to theEU15 countries, the pattern is opposite in the B scenarioswhere most dynamics occur in the EU15 countries.Of all changes in land use abandonment of agriculturalland is most important in terms of area (Table 4). While inTable 4Percentage of total land area of the EU that is expected to change due tourbanization, land abandonment or the development of new natureA1 A2 B1 B2Urbanization 2.37 1.38 1.33 0.41Land abandonmenta 6.35 2.49 6.28 5.21New natureb 2.11 0.55 4.58 3.18a This only includes abandoned agricultural land, not corrected for newagricultural areas at other locations.b This only includes the areas of new nature, not corrected for loss ofnature area at other locations.the A2 scenario 2.5% of the land area (which equalsapproximately 5% of the agricultural area in 2000) isabandoned this is 6.4% (approximately 13% of theagricultural area) in the A1 scenario where abandonmentof the current agricultural area is largest. Due to the smallexpansion of the agricultural in some parts of Europe in theGlobal Economy (A1) scenario the net loss of agriculturalarea is less than in the Global Co-operation (B1) scenario.Land abandonment puts an important issue concerningalternative uses on the agenda of to policy makers. Part of theabandoned land, especially in the A1 scenario, is used forresidential, industrial and recreation purposes. In all otherscenarios this is less and nature has possibilities to developon these lands. In the A scenarios nature development isassumed to occur only spontaneous; especially in theContinental Market (A2) scenario the extent of naturedevelopment is therefore very restricted. Under theconditions in the B scenarios active nature developmentleads to a large expansion of the natural areas, mainly onformer agricultural land. The lower urbanization ratesprovide opportunities for this development.Hotspots for agricultural land abandonment are typicallyfound in the neighborhood of important cities, where urbanpressure is high, or in areas that are surrounded by or bordernatural areas. These areas are mostly marginal areas foragriculture and easily abandoned in scenarios whereproduction efficiency increases. In the scenarios in whichnature development is an important issue, these areas are (asa consequence of location adjacent to nature areas and thelack of alternative uses) favoured for nature development(Fig. 4).Locations of areas where nature is lost differ by scenario.Hardly any hotspots of nature loss are identified since thelosses mostly are mainly the small patches within orbordering the agricultural areas. Hotspots for developmentof nature are often found in the neighborhood of existingnatural areas. This is most often due to abandonment ofagricultural lands on marginal soils bordering nature areas ordue to spatial policies such as the reinforcement of theNatura2000 conservation plan.Only a few locations are hotspot for urban growth in allscenarios: Paris, the Ruhrgebiet en Southern Poland. Theseareas are in 2000 already major urban areas, and as a resulturban growth is concentrated here. Locations that arehotspots for urban growth in three scenarios are moreabundant: they also are connected with major urban areas,like the Randstad, Lyon, the surroundings of Brussels andAntwerp, and Budapest. Dispersed urban growth is mainlyfound in the A scenarios but less frequent in the B scenariosdue to compact urbanization policies. The differences inurbanization hotspots are illustrated in Fig. 5 for the North-west European delta region (Belgium, the Netherlands).Urban development patterns differ between the scenarios notonly by urbanization strength, but also due to the differentspatial policies, e.g. the policies aiming at compactand Environment 114 (2006) 3956 49urbanization (see Table 1). In the A1 scenario, growth ofstemP.H. Verburg et al. / Agriculture, Ecosy50built-up area is highest due to the high growth of thepopulation, GDP and the industry and services sectors. Fig. 5shows that adjacent to existing urban areas large new urban/industrial areas are projected. At the same time a lot of urbansprawl is found in villages and small towns throughout thearea, especially in the green areas (due to e.g. theproliferation of second houses) and liberal spatial policies.In the A2 scenario we find a similar pattern. However, due tothe lower increase in built-up area the growth of the largercities is less striking. In the B1 and B2 scenarios the growthrate of built-up area in this part of Europe is much smaller.Still, large hotspots of urbanization directly adjacent to theexisting urban centres can be seen. This urbanization patternreflects the spatial policies aiming at compact urbanizationassumed for these scenarios.The overlap in simulated changes between the differentscenarios can be used to identify differences and similaritiesbetween the scenarios (Fig. 4). Some locations change ineach scenario: these are not dependent on the scenarioconditions and could be indicated as locations that changeindependently from differences in the spatial policies amongthe scenarios. Many other locations are only subject tochange in one or two scenarios, partly because of thedifferences in the demand for changes among the scenarios,but also because of the differences in spatial policies. Inaddition, the competition between the land use types differsFig. 4. Identification of hotspots of agricultural abandonment (20002030) andwhich the location is identified as a hotspot.s and Environment 114 (2006) 3956between the scenarios, leading to different path-dependentdevelopments that cause differences in resulting land usepatterns between the scenarios.The maximum possible overlap between locations ofchange is determined by the scenario with the least change.In Table 5 the maximum possible overlap is compared withthe real overlap. The maximum overlap for urbanization isvery much restricted due to the low urbanization rate in theRegional Communities (B2) scenario. However, in spite ofthe small area, only 73% of the area in the B2 scenario is alsourbanized in the scenarios where urbanization is moredominant. The overlap between the areas allocated to built-up area is partly a result of the tendency of the growthadjacent to the major urban centres in all scenarios. If theother scenarios are compared to scenario A1 that has thehighest rate of urbanization it is found that, respectively, 67and 58% of the new built-up area for the A2 and B1scenarios is allocated at the same locations as in the A1scenario. In the B1 scenario urbanization is assumed to bemore regulated by planning policies and therefore largedifferences with the A1 scenario appear. Land abandonmentand new nature are more different in spatial allocationbetween the scenarios. Only 39% of the new nature area inthe A2 scenario is also converted to nature in the otherscenarios. This is mainly due to the largely different spatialpolicies concerning nature protection and enforcement ofremaining agricultural areas. The colors indicate the number of scenarios inP.H. Verburg et al. / Agriculture, Ecosystems and Environment 114 (2006) 3956 51the Natura2000 network of protected areas versus theprotection of natural patches within the agricultural land-scapes.3.3. Landscape level impactsIn order to provide an assessment of the potential impactof the land use changes in the different scenarios onlandscape characteristics the results should be analysed inmore detail. Two examples of the impact on landscapevariability are discussed in this paper and illustrated withFigs. 6 and 7.The first example illustrates how non-linear changes indemand for arable land can have large impacts on thelandscape. Such non-linear changes in demand areFig. 5. New built-up areas (black) projected for 2030 and existing urban areas (grafor the different scenarios.Table 5Overlap in location for the main land use conversions on a European scaleMaximum overlap (% of land area)Urbanization 0.41Land abandonment 2.49New nature 0.55especially found in most accession countries for theconditions in the Global Co-operation (B1) scenario. Theresults of the macro-economic model calculations for thisscenario (see van Meijl et al., this issue) show an increase inthe arable land area until 2013 followed by a decrease in areauntil 2030 (Fig. 6d). This is due to changes in agriculturalpolicies after 2013: until that year the accession countriesare expected to benefit from European agricultural policiesand production quota, leading to an increase in arable area inthe first decade. This scenario, however, implies thatproduction quota are abolished which will result in astronger influence of liberalization after 2013, leading toabandonment of agricultural lands. As is illustrated inFig. 6b the increase in the area of arable land comes at thecost of a decrease in nature area during the first decade of they) for the North-western European delta region (Belgium/The Netherlands)Real overlap (% of land area) Ratio between real andmaximum overlap (%)0.30 731.03 410.21 39stemP.H. Verburg et al. / Agriculture, Ecosy52analysis. This is followed by a decrease of arable land arealeading to abandoned lands that are partly converted intonew natural areas (Fig. 6c). However, as can be seen from themaps, nature does not return at the locations where it is lostduring the first 10 years. During the first 10 years mainly thesmall patches of nature in the main agricultural areas are lost(see arrow in Fig. 6b), while new nature develops onabandoned, marginal lands, mostly adjacent to existingnature areas (arrow in Fig. 6c). This pathway of change hasimportant, irreversible consequences for the rural area andlandscape diversity in different parts of the country. Whilethe main agricultural areas are expected to loose theirremaining natural landscape elements and tend to becomemore homogeneous, the loss of agriculture in the moremarginal areas may have negative effects on the aestheticFig. 6. Aggregate changes in arable land area (d) and resulting land use patterns foroperation) scenario.s and Environment 114 (2006) 3956quality and diversity of these landscapes. Many regions inWestern European countries have followed a similartrajectory of land use change over the period 19602000in which many landscapes with high natural and culturalvalues were lost (Antrop, 2005).The second example concerns a region in SouthernFrance (Fig. 7). A large part of this region is currentlydesignated as a less favoured area. This means that in thisregion farmers are compensated for the less favourableagricultural conditions to prevent land abandonment, keepthe rural areas inhabited and protect the cultural landscapes.The large decreases in agricultural area pose an importantthreat to these areas. In the Global Co-operation (B1)scenario, where less favoured area support is expected to beabolished for arable agriculture a tremendous change in2000 (a), 2010 (b) and 2030 (c) in the Czech Republic for the B1 (Global Co-ystemsP.H. Verburg et al. / Agriculture, Ecoslandscape pattern is observed. Many marginal fields areabandoned and become part of the natural area, leading to anexpansion of the natural areas in this region. However, theloss of agricultural activities from these landscapes will haveimportant consequences for the character and quality of thelandscape. In the Regional Communities (B2) scenariosupport of farming activities in less favoured areas isassumed to be maintained. However, in spite of this support,not all agricultural land in the less favoured areas willremain in production due to the large decrease in the totalagricultural area. However, due to the less favoured areasupport it is expected that the remaining landscape patternwill still exhibit some of the variability in land usecharacteristic for the current landscape in this area.Therefore, the changes in landscape in this area are muchless drastic in this scenario. The differential impacts onlandscapes are a demonstration of the impact macro-economic changes may have on regional land use patterns.4. DiscussionThe procedure described in this paper has been successfulin downscaling a coarse scale assessment of changes in landFig. 7. Land use pattern in 2000 and 2030 for the B1 (Global Co-operation) and Bright corner is Marseille).and Environment 114 (2006) 3956 53use to region-specific changes in land use pattern. The highspatial resolution visualizes the consequences of thesechanges for the different regions within the countries andreveals the enormous spatial variability in impact on thelandscape. The land use model accounts for spatial andtemporal interactions and allows specific driving factors fordifferent countries and scenarios. In interaction with themacro-level changes in land demand varying, dynamic,spatial patterns of land use change emerge and it becomesclear how policies affect landscapes in different contexts.Although the representation of landscape level changes isstill relatively coarse as a consequence of the spatialresolution as compared to the resolution used in landscapelevel case studies, the approach provides an opportunity tobridge European level assessments and local case studies.The type of landscape processes (fragmentation, abandon-ment, urban encroachment, etc.) that are observed anddescribed in local case studies can be identified from thesimulation results. Furthermore, from the maps it is possibleto estimate in which parts of Europe similar processes ofchange in landscape pattern are expected and, therefore, givean indication of the general validity of the case study. In thissense, the approach can be used as a framework to link localcase studies that add more depth in understanding the2 (Regional Communities) scenario for Southern France (city in the lowerstemprocesses of landscape change. It should be noted that,without additional information, the method only addresseschanges in landscape in terms of changes in land use pattern.Following the definition of Wascher (2004) landscapes arespatial units whose character and functions are defined bythe complex and region-specific interaction of naturalprocesses with human activities that are driven byeconomic, social and environmental forces and values.Therefore, a full analysis of landscape change needsadditional information on the social and economic impactsof change in order to obtain a comprehensive assessment ofthe landscape.The visualization of changes in land use pattern fordifferent scenarios can support policy discussions on thedevelopment of the European landscape, support theidentification of priority areas for intervention and test thepotential consequences of certain policy options. Althoughtechnically it is possible to calculate the consequences ofindividual spatial policies on land use patterns, such anapproach may not be consistent with the scenario approach.Scenarios are commonly developed, as much as possible, asinternally consistent storylines (Rotmans et al., 2000; Xiangand Clarke, 2003; Shearer, 2005). Variations in a certainpolicy may not be consistent with the basic ideas underlyingthe scenario and conflict with the socio-economic andpolitical assumptions of the storyline. Therefore, thesensitivity of the land use patterns to specific policies canonly be explored as far as such a variation is acceptablewithin the overall storyline of the scenario.An analysis of the results for the four scenarios presentedin this study reveals that land abandonment is likely tobecome an important issue for land use in Europe. Manycase studies in different parts of Europe indicate that alreadyin the current situation land abandonment is a commonphenomenon (MacDonald et al., 2000; Eetvelde and Antrop,2004; Kristensen et al., 2004). In the simulation results theseabandoned arable lands are classified as abandoned land or,after some years, as natural area if active nature managementor spontaneous regrowth is assumed. However, this does notclarify the actual use of the abandoned lands. Part of theselands may still have some extensive agricultural functions,as some farmers have compensated the loss of income asresult of agricultural policy reforms by additional activitiesoutside agriculture. Such agricultural lands may remainunder extensive forms of agriculture as result of part-timeor hobby farming. Other abandoned lands may transforminto estates with houses for the rich or obtain recreationalfunctions. Another option not considered in this study is theuse of such lands for the cultivation of biofuels. Biofuelcultivation may become an interesting option whenabundant land is available and may compete with theconversion of abandoned agricultural lands to nature. Asindicated by other authors studying developments inEuropean land use (Vereijken, 2002; Rounsevell et al.,2005), the future function of the areas that become availableP.H. Verburg et al. / Agriculture, Ecosy54due to agricultural abandonment poses an enormouschallenge to planners and policy makers to find optionsthat best preserve the quality and identity of the landscapes.Scenario simulations can help to support the discussion onthis issue.The method presented in this study can provide a cruciallink between global to national scale assessments of land usechange and the landscape level impacts. The results allow anin-depth assessment of the consequences of the simulatedland use changes for different aspects of the landscape. Thespatial patterns of changes in natural areas make anassessment of the consequences for biodiversity possible.The changes in the size of continuous natural areas caneasily be determined and used in biodiversity assessmentsbased on the areaspecies curve (McIntosh, 1985; Scheiner,2003). For agricultural biodiversity the assessments aremore complicated. Although the results do not provideinformation on the actual changes in landscape structurerelevant to biodiversity, e.g. the removal or creation oflandscape elements such as hedgerows and small naturalpatches in the agricultural landscape (Baudry et al., 2003),the loss or gain of pixels classified as nature within theagricultural areas provides an indication of the changes inlandscape pattern relevant to biodiversity assessments(Reidsma et al., this issue). Similar assessments are possiblefor a large number of other environmental and socialindicators, including greenhouse gas emissions, landscapediversity, etc.A major limitation of all assessments based on these datais the lack of information on the intensity of the land use.Currently, agricultural practices differ strongly bothbetween different regions in Europe and within regions.Also in the scenarios major changes are expected in cropproductivity and farming intensity, with significantlydifferent developments for the different scenarios. Thetransition into organic farming systems and multi-func-tional agricultural landscapes will face varying opportu-nities in the different scenarios. In many areasintensification and extensification or abandonment happenside by side (Klijn and Vos, 2000; Eetvelde and Antrop,2004). Such changes in farming intensity have enormousimpact on the landscape and environmental issues (ground-water pollution, etc.). In the current application changes incrop productivity have been accounted for in the calcula-tions with the integrated assessment model at the nationalscale (van Meijl et al., this issue), but has not been includedin the spatial allocation procedure. A major constraint forincluding this is the availability of high resolution data onfarming systems and production intensity. For adminis-trative units production data are available that may givesome indication, but data on crop types and associatedlivestock systems (e.g. grazing intensities) are needed for adetailed assessment.The scenario approach includes a range of spatialpolicies that are relevant for the whole European extent.However, in the current situation spatial policies tend tos and Environment 114 (2006) 3956differ by country due to the influence of national levelHowever, for a more explicit specification detailed researchagrarian landscapes in the 1990s: the interaction between farmers andthe farmed landscape. A case study from Jutland, Denmark. J. Environ.ystems and Environment 114 (2006) 3956 555. ConclusionThe method presented in this paper allows the down-scaling of coarse scale land use change assessments to thelandscape level. The method results in the visualization ofthe effect of the scenario conditions on land use patterns,allows semi-quantitative analysis of effects on landscapesand associated indicators and links continental scaleassessments with local case studies. Such a linkagebetween different scales is essential, since in this typeof land use change assessments the relations between thesimulated changes and the actual processes of change arenot obvious, as is the way they cause a profound change ofthe landscape character and identity. Remaining challengesare the further downscaling of the simulated land coverchanges to the fundamental determinants of the land-scapes, including the field size and structure, managementintensity and landscape elements. Such assessment oflandscape change trajectories could be linked to the currentdownscaling procedure and complement the toolbox todiscuss the future of Europes landscape and spatialplanning policies.AcknowledgementThis study has been conducted as part of the EUR-into the planning traditions of the different countries wouldbe needed.The validity of the model results is an issue not addressedin this paper. In this respect it should be noted that thesimulation results are not meant as predictions of future landuse but as projections based on the assumed scenarioconditions, or rather, as a quantified, visualization of thequalitative scenario descriptions. However, validation couldstill contribute to an assessment of the validity anduncertainty in the downscaling procedure. Althoughdifferent versions of the CLUE model have been validatedwith good results in different applications (Kok et al., 2001;Verburg et al., 2002), the validity of a model is mainlydetermined by the case study specific characteristics and thequality of the input data. Therefore, a proper validation ofthe model simulations in this study can only be based onEuropean land use data. This requires consistent land coverdatabases for 2 years, which are hardly available for theEuropean extent. The new CORINE database that highlightschanges in land cover between 1990 and 2000 of theEuropean Environmental Agency will make such avalidation possible.policies that, in some of the scenarios, certainly will remainimportant. To some extent these policies are captured by thecountry-specific specification of the driving factors.P.H. Verburg et al. / Agriculture, EcosURALIS project commissioned by the Dutch Ministry ofManage. 71, 231244.Lambin, E.F., Geist, H.J., 2003. 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B 30, 885909.Downscaling of land use change scenarios to assess the dynamics of European landscapesIntroductionMethods and dataOverview of the approachLand use change modelLand requirementsLocation suitabilityLand use type specific conditionsArea specific conditionsDynamic simulation of scenariosDataResultsScenario specific resultsGlobal Economy (A1)Continental Market (A2)Global Co-operation (B1)Regional Communities (B2)Comparison of the scenariosLandscape level impactsDiscussionConclusionAcknowledgementReferences

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